A Comparison Of Compressed Sensing And Dnn Based Reconstruction For Ghost Motion Imaging

Abstract

Ghost imaging is a technique that enables producing object’s images without a multi-pixel detector. In a recently demonstrated technique called ghost motion imaging (GMI), images of objects under motion across an optical structure are encoded into corresponding signals observed by a single-pixel detector, and the object images can be reconstructed from the signals. GMI has been shown to be applicable to high-throughput cell morphometry. Image reconstruction for GMI was previously implemented by mean of a two-step iterative shrinkage/thresholding (TwIST) algorithm in the compressed sensing framework. In this work, we propose a learning-based image reconstruction from the GMI signals by using a deep neural network (DNN). We found that our DNN-based method is more accurate in image reconstruction with a shorter signal measurement than the TwIST-based one.